(
Aim
) COVID-19 has caused more than 2.28 million deaths till 4/Feb/2021 while it is still spreading across the world. This study proposed a novel artificial intelligence model to diagnose COVID-19 based on chest CT images. (
Methods
) First, the two-dimensional fractional Fourier entropy was used to extract features. Second, a custom
deep stacked sparse autoencoder (DSSAE)
model was created to serve as the classifier. Third, an improved multiple-way data augmentation was proposed to resist overfitting. (
Results
) Our DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem (COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control). (
Conclusion
) Our method outperforms 10 state-of-the-art approaches.
Gingivitis detection by fractional Fourier entropy with optimization of hidden neurons
